Decorrelation-Based Feature Discovery: Activity-Whole

1 Effect of UPSTM-Based Decorrelation on Feature Discovery

1.0.1 Loading the libraries

library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)

op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

1.1 Material and Methods

Data Source https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960825/

Mohino-Herranz I, Gil-Pita R, Rosa-Zurera M, Seoane F. Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study. Sensors (Basel). 2019 Dec 13;19(24):0. doi: 10.3390/s19245524. PMID: 31847261; PMCID: PMC6960825.

1.2 The Data

Activitydata <- read.csv("~/GitHub/LatentBiomarkers/Data/ActivityData/data.txt", header=FALSE, stringsAsFactors=TRUE)
featNames <- read.table("~/GitHub/LatentBiomarkers/Data/ActivityData/Featurelabels.txt", quote="\"", comment.char="")
featNames <- as.character(t(featNames))
featNames <- str_replace_all(featNames,"\\(abs\\)","_A_")
featNames[duplicated(featNames)] <- paste(featNames[duplicated(featNames)],"D",sep="_")

rep_ID <- numeric(nrow(Activitydata))
ctr <- 1
for (ind in c(1:(nrow(Activitydata)-1)))
{
  rep_ID[ind] <- ctr
  if (Activitydata$V1[ind] != Activitydata$V1[ind+1]) ctr <- 0;
  ctr <- ctr + 1
}
rownames(Activitydata) <- paste(Activitydata$V1,rep_ID,sep="_")
colnames(Activitydata) <- c("ID",featNames,"class")
Activitydata$rep <- rep_ID
  
tb <- table(Activitydata$class)

classes <- c("Neu","Emo","Men","Phy")
names(classes) <- names(tb)
Activitydata$class <- classes[as.character(Activitydata$class)]
table(Activitydata$class)
#> 
#>  Emo  Men  Neu  Phy 
#> 1120 1120 1120 1120



ID_class <- paste(Activitydata$ID,Activitydata$class)
IDCLASS <- unique(ID_class)
theclass <- Activitydata$class[!duplicated(ID_class)]
theIDs <- Activitydata$ID[!duplicated(ID_class)]

ActivitydataAvg <- NULL
for (id in IDCLASS)
{
  ActivitydataAvg <- rbind(ActivitydataAvg,apply(Activitydata[ID_class==id,featNames],2,mean))
}
colnames(ActivitydataAvg) <- featNames
rownames(ActivitydataAvg) <- IDCLASS
ActivitydataAvg <- as.data.frame(ActivitydataAvg)
ActivitydataAvg$class <- theclass
ActivitydataAvg$ID <- theIDs

table(ActivitydataAvg$class)
#> 
#> Emo Men Neu Phy 
#>  40  40  40  40


ActivitydataAvg <- subset(ActivitydataAvg, class=="Men" | class=="Emo")

ActivitydataAvg$class <- 1*(ActivitydataAvg$class == "Men")
table(ActivitydataAvg$class)
#> 
#>  0  1 
#> 40 40

1.2.0.1 Standarize the names for the reporting

studyName <- "Activity"
dataframe <- ActivitydataAvg
outcome <- "class"

TopVariables <- 10
thro <- 0.80
cexheat = 0.15

1.3 Generaring the report

1.3.1 Libraries

Some libraries

library(psych)
library(whitening)
library("vioplot")
library("rpart")

1.3.2 Data specs

pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))

rows	col
80	534

pander::pander(table(dataframe[,outcome]))

0	1
40	40

varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]

largeSet <- length(varlist) > 1500 

1.3.3 Scaling the data

Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns

  ### Some global cleaning
  sdiszero <- apply(dataframe,2,sd) > 1.0e-16
  dataframe <- dataframe[,sdiszero]

  varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
  tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
  dataframe <- dataframe[,tokeep]

  varlist <- colnames(dataframe)
  varlist <- varlist[varlist != outcome]
  
  iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples



dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData

1.4 The heatmap of the data

numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000


if (!largeSet)
{

  hm <- heatMaps(data=dataframeScaled[1:numsub,],
                 Outcome=outcome,
                 Scale=TRUE,
                 hCluster = "row",
                 xlab="Feature",
                 ylab="Sample",
                 srtCol=45,
                 srtRow=45,
                 cexCol=cexheat,
                 cexRow=cexheat
                 )
  par(op)
}

1.4.0.1 Correlation Matrix of the Data

The heat map of the data

if (!largeSet)
{

  par(cex=0.6,cex.main=0.85,cex.axis=0.7)
  #cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
  cormat <- cor(dataframe[,varlist],method="pearson")
  cormat[is.na(cormat)] <- 0
  gplots::heatmap.2(abs(cormat),
                    trace = "none",
  #                  scale = "row",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Original Correlation",
                    cexRow = cexheat,
                    cexCol = cexheat,
                     srtCol=45,
                     srtRow=45,
                    key.title=NA,
                    key.xlab="|Pearson Correlation|",
                    xlab="Feature", ylab="Feature")
  diag(cormat) <- 0
  print(max(abs(cormat)))
}

[1] 1

1.5 The decorrelation

DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#> 
#>  Included: 362 , Uni p: 0.0184537 , Uncorrelated Base: 33 , Outcome-Driven Size: 0 , Base Size: 33 
#> 
#> 
 1 <R=1.000,r=0.975,N=  255>, Top: 43( 1 )[ 1 : 43 Fa= 42 : 0.975 ]( 42 , 169 , 0 ),<|>Tot Used: 211 , Added: 169 , Zero Std: 0 , Max Cor: 1.000
#> 
 2 <R=1.000,r=0.975,N=  255>, Top: 32( 1 )[ 1 : 32 Fa= 70 : 0.975 ]( 32 , 95 , 42 ),<|>Tot Used: 242 , Added: 95 , Zero Std: 0 , Max Cor: 1.000
#> 
 3 <R=1.000,r=0.975,N=  255>, Top: 14( 2 )[ 1 : 14 Fa= 82 : 0.975 ]( 14 , 28 , 70 ),<|>Tot Used: 246 , Added: 28 , Zero Std: 0 , Max Cor: 1.000
#> 
 4 <R=1.000,r=0.975,N=  255>, Top: 8( 8 )[ 1 : 8 Fa= 89 : 0.975 ]( 8 , 23 , 82 ),<|>Tot Used: 246 , Added: 23 , Zero Std: 0 , Max Cor: 1.000
#> 
 5 <R=1.000,r=0.950,N=  115>, Top: 41( 4 )[ 1 : 41 Fa= 101 : 0.950 ]( 38 , 62 , 89 ),<|>Tot Used: 274 , Added: 62 , Zero Std: 0 , Max Cor: 1.000
#> 
 6 <R=1.000,r=0.950,N=  115>, Top: 14( 1 )[ 1 : 14 Fa= 109 : 0.950 ]( 14 , 18 , 101 ),<|>Tot Used: 274 , Added: 18 , Zero Std: 0 , Max Cor: 1.000
#> 
 7 <R=1.000,r=0.950,N=  115>, Top: 3( 4 )[ 1 : 3 Fa= 110 : 0.950 ]( 3 , 6 , 109 ),<|>Tot Used: 274 , Added: 6 , Zero Std: 0 , Max Cor: 1.000
#> 
 8 <R=1.000,r=0.900,N=  134>, Top: 48( 4 )[ 1 : 48 Fa= 117 : 0.900 ]( 44 , 69 , 110 ),<|>Tot Used: 295 , Added: 69 , Zero Std: 0 , Max Cor: 1.000
#> 
 9 <R=1.000,r=0.900,N=  134>, Top: 11( 4 )[ 1 : 11 Fa= 120 : 0.900 ]( 9 , 13 , 117 ),<|>Tot Used: 299 , Added: 13 , Zero Std: 0 , Max Cor: 1.000
#> 
 10 <R=1.000,r=0.850,N=  109>, Top: 45( 3 )[ 1 : 45 Fa= 138 : 0.860 ]( 44 , 59 , 120 ),<|>Tot Used: 321 , Added: 59 , Zero Std: 0 , Max Cor: 0.995
#> 
 11 <R=0.995,r=0.847,N=  109>, Top: 8( 2 )[ 1 : 8 Fa= 140 : 0.847 ]( 8 , 9 , 138 ),<|>Tot Used: 321 , Added: 9 , Zero Std: 0 , Max Cor: 0.961
#> 
 12 <R=0.961,r=0.800,N=   99>, Top: 40( 2 )[ 1 : 40 Fa= 145 : 0.800 ]( 39 , 55 , 140 ),<|>Tot Used: 329 , Added: 55 , Zero Std: 0 , Max Cor: 0.982
#> 
 13 <R=0.982,r=0.800,N=   99>, Top: 15( 1 )[ 1 : 15 Fa= 148 : 0.800 ]( 15 , 20 , 145 ),<|>Tot Used: 330 , Added: 20 , Zero Std: 0 , Max Cor: 0.999
#> 
 14 <R=0.999,r=0.800,N=   99>, Top: 6( 1 )[ 1 : 6 Fa= 151 : 0.800 ]( 6 , 6 , 148 ),<|>Tot Used: 330 , Added: 6 , Zero Std: 0 , Max Cor: 0.966
#> 
 15 <R=0.966,r=0.800,N=    2>, Top: 1( 1 )[ 1 : 1 Fa= 152 : 0.800 ]( 1 , 1 , 151 ),<|>Tot Used: 330 , Added: 1 , Zero Std: 0 , Max Cor: 0.800
#> 
 16 <R=0.800,r=0.800,N=    0>
#> 
 [ 16 ], 0.8965018 Decor Dimension: 330 Nused: 330 . Cor to Base: 197 , ABase: 16 , Outcome Base: 0 
#> 
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]

pander::pander(sum(apply(dataframe[,varlist],2,var)))

2.43e+21

pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))

5.08e+14

pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))

0.0195

pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))

0.0691

1.5.1 The decorrelation matrix

if (!largeSet)
{

  par(cex=0.6,cex.main=0.85,cex.axis=0.7)
  
  UPSTM <- attr(DEdataframe,"UPSTM")
  
  gplots::heatmap.2(1.0*(abs(UPSTM)>0),
                    trace = "none",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Decorrelation matrix",
                    cexRow = cexheat,
                    cexCol = cexheat,
                   srtCol=45,
                   srtRow=45,
                    key.title=NA,
                    key.xlab="|Beta|>0",
                    xlab="Output Feature", ylab="Input Feature")
  
  par(op)
}

1.6 The heatmap of the decorrelated data

if (!largeSet)
{

  hm <- heatMaps(data=DEdataframe[1:numsub,],
                 Outcome=outcome,
                 Scale=TRUE,
                 hCluster = "row",
                 cexRow = cexheat,
                 cexCol = cexheat,
                 srtCol=45,
                 srtRow=45,
                 xlab="Feature",
                 ylab="Sample")
  par(op)
}

1.7 The correlation matrix after decorrelation

if (!largeSet)
{

  cormat <- cor(DEdataframe[,varlistc],method="pearson")
  cormat[is.na(cormat)] <- 0
  
  gplots::heatmap.2(abs(cormat),
                    trace = "none",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Correlation after IDeA",
                    cexRow = cexheat,
                    cexCol = cexheat,
                     srtCol=45,
                     srtRow=45,
                    key.title=NA,
                    key.xlab="|Pearson Correlation|",
                    xlab="Feature", ylab="Feature")
  
  par(op)
  diag(cormat) <- 0
  print(max(abs(cormat)))
}

[1] 1

1.8 U-MAP Visualization of features

1.8.1 The UMAP based on LASSO on Raw Data

if (nrow(dataframe) < 1000)
{
  classes <- unique(dataframe[1:numsub,outcome])
  raincolors <- rainbow(length(classes))
  names(raincolors) <- classes
  datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
  plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
  text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}

1.8.2 The decorralted UMAP

if (nrow(dataframe) < 1000)
{

  datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
  plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
  text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}

1.9 Univariate Analysis

1.9.1 Univariate

univarRAW <- uniRankVar(varlist,
               paste(outcome,"~1"),
               outcome,
               dataframe,
               rankingTest="AUC")

100 : ECG_p_LF_mean 200 : IT_CCV_LF 300 : EDA_Original_mad_D

univarDe <- uniRankVar(varlistc,
               paste(outcome,"~1"),
               outcome,
               DEdataframe,
               rankingTest="AUC",
               )

100 : La_ECG_p_LF_mean 200 : La_IT_CCV_LF 300 : La_EDA_Original_mad_D

1.9.2 Final Table

univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")

##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)

	caseMean	caseStd	controlMean	controlStd	controlKSP	ROCAUC
ECG_hrv_prctile75	-2.719	8.9741	-5.8408	10.9387	0.00378	0.731
ECG_hrv_geomean_A_	10.370	8.0477	13.8743	7.8682	0.00822	0.727
IT_LF_baseline_D	43.569	25.1600	26.4345	15.3449	0.61672	0.721
IT_p_Total_baseline	51.974	29.4230	31.5389	17.9800	0.71076	0.721
IT_VLF_baseline	57.578	32.3488	34.9418	19.7865	0.75500	0.720
ECG_hrv_prctile25	-12.471	7.7963	-16.6349	6.7770	0.20654	0.719
IT_PSD_baseline	0.059	0.0376	0.0358	0.0228	0.21000	0.715
ECG_hrv_mean	-7.294	5.4920	-11.0369	6.0390	0.33833	0.714
IT_HF_baseline	3.308	3.5341	2.0033	2.1158	0.00564	0.713
ECG_hrv_trimmean25	-7.619	6.1613	-11.3761	6.3193	0.35724	0.711

topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]


pander::pander(finalTable)

	caseMean	caseStd	controlMean	controlStd	controlKSP	ROCAUC
La_ECG_RR_window_baseline	19.766	11.454	10.652	10.519	0.62646	0.776
La_EDA_Original_mad_D	-7.578	10.562	1.170	9.564	0.01215	0.776
La_ECG_HR_min_div_std	0.533	0.674	1.130	0.819	0.22297	0.762
La_IT_BRV_baseline	-3.930	2.265	-2.114	1.544	0.80056	0.756
La_EDA_Original_baseline_D	-558.607	1630.312	714.724	1166.346	0.03382	0.746
La_ECG_RR_window_harmmean	0.595	0.380	0.375	0.411	0.04531	0.734
La_EDA_Original_max_D	127.623	285.226	-44.023	189.364	0.00307	0.731
La_ECG_RR_window_mad	5.227	3.767	8.361	5.205	0.64092	0.722
La_EDA_processed_trimmean25_D	0.999	2.270	-0.379	1.094	0.15207	0.721
La_EDA_Original_std_D	-15.390	75.369	74.185	136.981	0.01610	0.719

dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")


pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))

mean	total	fraction
3.33	301	0.825

theCharformulas <- attr(dc,"LatentCharFormulas")


finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])


orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]

Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")

finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])

	DecorFormula	caseMean	caseStd	controlMean	controlStd	controlKSP	ROCAUC	RAWAUC	fscores
La_ECG_RR_window_baseline	- (0.927)ECG_RR_window_mean + ECG_RR_window_baseline	19.766	1.15e+01	10.6516	1.05e+01	0.62646	0.776	0.522	-1
La_EDA_Original_mad_D	- (0.831)EDA_Original_std_D + EDA_Original_mad_D	-7.578	1.06e+01	1.1699	9.56e+00	0.01215	0.776	0.507	0
La_ECG_HR_min_div_std	+ ECG_HR_min_div_std - (2.612)ECG_hrv_std + (1.827)ECG_hrv_mad	0.533	6.74e-01	1.1297	8.19e-01	0.22297	0.762	0.579	-2
La_IT_BRV_baseline	- (0.588)IT_BRV_mean + IT_BRV_baseline	-3.930	2.27e+00	-2.1142	1.54e+00	0.80056	0.756	0.642	-1
La_EDA_Original_baseline_D	- (0.895)EDA_Original_mean_D + EDA_Original_baseline_D	-558.607	1.63e+03	714.7236	1.17e+03	0.03382	0.746	0.504	-1
La_ECG_RR_window_harmmean	+ (0.715)ECG_RR_window_mean - (1.710)ECG_RR_window_geomean_A_ + ECG_RR_window_harmmean + (0.702)ECG_hrv_mean - (0.664)ECG_hrv_trimmean25	0.595	3.80e-01	0.3748	4.11e-01	0.04531	0.734	0.603	-3
La_EDA_Original_max_D	- (4.171)EDA_Original_mean_D + EDA_Original_max_D + (3.174)EDA_Original_prctile25_D	127.623	2.85e+02	-44.0231	1.89e+02	0.00307	0.731	0.575	-2
ECG_hrv_prctile75	NA	-2.719	8.97e+00	-5.8408	1.09e+01	0.00378	0.731	0.731	NA
ECG_hrv_geomean_A_	NA	10.370	8.05e+00	13.8743	7.87e+00	0.00822	0.727	0.727	NA
La_ECG_RR_window_mad	+ ECG_RR_window_mad - (1.741)ECG_hrv_mad	5.227	3.77e+00	8.3609	5.20e+00	0.64092	0.722	0.637	2
La_EDA_processed_trimmean25_D	+ EDA_processed_trimmean25_D - (0.652)EDA_processed_median_D	0.999	2.27e+00	-0.3787	1.09e+00	0.15207	0.721	0.582	-1
IT_LF_baseline_D	NA	43.569	2.52e+01	26.4345	1.53e+01	0.61672	0.721	0.721	NA
IT_p_Total_baseline	NA	51.974	2.94e+01	31.5389	1.80e+01	0.71076	0.721	0.721	NA
IT_VLF_baseline	NA	57.578	3.23e+01	34.9418	1.98e+01	0.75500	0.720	0.720	NA
ECG_hrv_prctile25	NA	-12.471	7.80e+00	-16.6349	6.78e+00	0.20654	0.719	0.719	NA
La_EDA_Original_std_D	+ EDA_Original_std_D - (2.861)EDA_processed_std_D	-15.390	7.54e+01	74.1854	1.37e+02	0.01610	0.719	0.510	3
IT_PSD_baseline	NA	0.059	3.76e-02	0.0358	2.28e-02	0.21000	0.715	0.715	4
ECG_hrv_mean	NA	-7.294	5.49e+00	-11.0369	6.04e+00	0.33833	0.714	0.714	5
IT_HF_baseline	NA	3.308	3.53e+00	2.0033	2.12e+00	0.00564	0.713	0.713	NA
ECG_hrv_trimmean25	NA	-7.619	6.16e+00	-11.3761	6.32e+00	0.35724	0.711	0.711	NA

1.10 Comparing IDeA vs PCA vs EFA

1.10.1 PCA

featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE)   #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous]) 
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)

#pander::pander(pc$rotation)


PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])


  gplots::heatmap.2(abs(PCACor),
                    trace = "none",
  #                  scale = "row",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "PCA Correlation",
                    cexRow = 0.5,
                    cexCol = 0.5,
                     srtCol=45,
                     srtRow= -45,
                    key.title=NA,
                    key.xlab="Pearson Correlation",
                    xlab="Feature", ylab="Feature")

1.10.2 EFA

EFAdataframe <- dataframeScaled

if (length(iscontinous) < 2000)
{
  topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
  if (topred < 2) topred <- 2
  
  uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE)  # EFA analysis
  predEFA <- predict(uls,dataframeScaled[,iscontinous])
  EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
  colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous]) 


  
  EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
  
  
    gplots::heatmap.2(abs(EFACor),
                      trace = "none",
    #                  scale = "row",
                      mar = c(5,5),
                      col=rev(heat.colors(5)),
                      main = "EFA Correlation",
                      cexRow = 0.5,
                      cexCol = 0.5,
                       srtCol=45,
                       srtRow= -45,
                      key.title=NA,
                      key.xlab="Pearson Correlation",
                      xlab="Feature", ylab="Feature")
}

1.11 Effect on CAR modeling

par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")

  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(rawmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
  }

pander::pander(table(dataframe[,outcome],pr))

	0	1
0	38	2
1	7	33

pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.887	0.797	0.947
3	se	0.825	0.672	0.927
4	sp	0.950	0.831	0.994
6	diag.or	89.571	17.389	461.391

par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")

  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(IDeAmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
  }

pander::pander(table(DEdataframe[,outcome],pr))

	0	1
0	34	6
1	3	37

pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.887	0.797	0.947
3	se	0.925	0.796	0.984
4	sp	0.850	0.702	0.943
6	diag.or	69.889	16.198	301.551

par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
  plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
  text(PCAmodel, use.n = TRUE,cex=0.75)
  ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}

pander::pander(table(PCAdataframe[,outcome],pr))

	0	1
0	26	14
1	4	36

pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.775	0.668	0.861
3	se	0.900	0.763	0.972
4	sp	0.650	0.483	0.794
6	diag.or	16.714	4.933	56.633

par(op)

1.11.1 EFA

  EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
  EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
  pr <- predict(EFAmodel,EFAdataframe,type = "class")
  
  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(EFAmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
  }

  pander::pander(table(EFAdataframe[,outcome],pr))

	0	1
0	29	11
1	6	34

  pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.787	0.682	0.871
3	se	0.850	0.702	0.943
4	sp	0.725	0.561	0.854
6	diag.or	14.939	4.917	45.389

  par(op)